Rabeprazole Combined with Hydrotalcite is Effective for Patients with Bile Reflux Gastritis after Cholecystectomy
Bibliographic record
Abstract
BACKGROUND: Regardless of surgical technique, patients who have undergone cholecystectomy appear to be predisposed to the development of bile reflux gastritis. OBJECTIVE: To assess the efficacy of rabeprazole and hydrotalcite in patients with bile reflux gastritis after cholecystectomy. METHODS: Postcholecystectomy patients with bile reflux gastritis confirmed by endoscopy and 24 h gastric bilirubin monitoring were randomly assigned to one of four eight-week treatments: observation (group A), rabeprazole alone (group B), hydrotalcite alone (group C) and rabeprazole in combination with hydrotalcite (group D). Endoscopy and 24 h gastric bilirubin monitoring were repeated in all patients after treatment. Dyspeptic symptoms of abdominal pain, bloating, heartburn, bitter taste, endoscopic and histological finding, and biliary reflux were evaluated before and after treatment. RESULTS: After administering medication, patient symptoms in groups B, C and D were relieved - most significantly in group D (P<0.05). There were no significant differences in endoscopic hyperemia and histological inflammation among the groups (P>0.05). However, histological activity, the number of reflux episodes and the number of reflux episodes lasting longer than 5 min were significantly decreased only in group D (P<0.05). The total per cent of bilirubin absorption (value of 0.14 units or greater) time was decreased in groups B, C and D, and most significantly in group D (P<0.05). CONCLUSION: Rabeprazole combined with hydrotalcite is an effective therapeutic option in the treatment of patients with bile reflux gastritis after cholecystectomy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".